Multicompartment body composition analysis in older adults: a cross-sectional study

Area of Science:

• Gerontology
• Human Physiology
• Biostatistics

Background:

• Aging significantly alters body composition, impacting health, quality of life, and functional capacity.
• Existing anthropometric models (2-C) have limitations in predicting fat mass (FM) and fat-free mass (FFM) due to factors like bone demineralization and FFM hydration changes.
• Accurate body composition assessment is crucial for understanding and managing age-related health declines.

Purpose of the Study:

• To develop and validate a novel multi-compartmental anthropometric model for predicting fat, bone, and musculature in older adults.
• To address the limitations of existing bi-compartmental models in aging populations.
• To provide a practical tool for monitoring body composition changes during senescence.

Main Methods:

• A cross-sectional study involving 100 older adults (both sexes).
• Dual-energy X-ray absorptiometry (DXA) was used to measure fat mass (FM), bone mineral content (BMC), and appendicular lean soft tissue (ALST).
• Multivariate linear regression analyzed 29 anthropometric measures and sex as predictors, with PRESS statistic for validation.

Main Results:

• A predictive equation was derived using weight, half-arm span (HAS), triceps skinfold (TriSK), and sex.
• The model demonstrated high predictive accuracy for FM (R²adj: 0.83), BMC (R²adj: 0.61), and ALST (R²adj: 0.85).
• Low estimation errors were observed across all predicted components.

Conclusions:

• The developed anthropometric equations offer a reliable, practical, and low-cost method for monitoring body composition in older adults.
• The model serves as an inexpensive alternative for clinical field use, validated by the PRESS statistic.
• This tool can aid in tracking age-related changes in body components and their health implications.